S64 Long SOA Sem Pri Orthographic Facilitation
#Pre-Processing
##Importing and examining the trial-level dataset
##Import
###########
#Imports the data file and saves it into the global environment.
#Gets rid of any extra columns with an X
#Directory and File_Name should be in " "
#directory is "C:/Users/Alexander/Desktop/OrthFacil/RCode/"
Importing_and_examining <- function(directory, File_Name){
setwd(directory) #set working directory to folder with files
experiment <- list.files(pattern="*.txt") #Get name of the experiment data file
filepath <- file.path(directory, experiment) #Get filepath
Compiled <- read.delim(filepath, header =TRUE) #Import file specified by filepath
assign(File_Name, Compiled, envir = .GlobalEnv)
return(str(Compiled[1:10,]))
}
Importing_and_examining("C:/Users/Alexander/Desktop/S64_LongSOASemPri_OrthFacil/SemPri_OrthFacil_RCode", "Compiled")'data.frame': 10 obs. of 20 variables:
$ Computer : Factor w/ 6 levels "1","2","3","BA",..: 6 6 6 6 6 6 6 6 6 6
$ Group : Factor w/ 4 levels "L1","L2","L3",..: 1 1 1 1 1 1 1 1 1 1
$ Count : int 1 2 3 4 5 6 7 8 9 10
$ Subject : int 1 1 1 1 1 1 1 1 1 1
$ RefreshRate: Factor w/ 3 levels "16.58ms","16.62ms",..: 3 3 3 3 3 3 3 3 3 3
$ Item : int 705 706 707 708 701 712 702 703 704 709
$ RT : num 717 748 -649 611 622 ...
$ Target : Factor w/ 140 levels "ACHE","BEAL",..: 140 38 101 100 27 34 109 120 115 54
$ List : int 0 0 0 0 0 0 0 0 0 0
$ Lexicality : Factor w/ 3 levels "nonword","practice",..: 2 2 2 2 2 2 2 2 2 2
$ OrthPrime : Factor w/ 199 levels "achb","baqt",..: 156 124 16 131 125 171 162 164 146 73
$ Orth_Rel : Factor w/ 3 levels "practice","related",..: 1 1 1 1 1 1 1 1 1 1
$ SemPrime : Factor w/ 203 levels "adorable","all",..: 135 84 61 118 5 86 23 66 76 13
$ Sem_Rel : Factor w/ 4 levels "NONE","practice",..: 2 2 2 2 2 2 2 2 2 2
$ Trial.Type : Factor w/ 3 levels "correct","incorrect",..: 3 3 3 3 3 3 3 3 3 3
$ X : logi NA NA NA NA NA NA ...
$ X.1 : logi NA NA NA NA NA NA ...
$ X.2 : logi NA NA NA NA NA NA ...
$ X.3 : logi NA NA NA NA NA NA ...
$ X.4 : logi NA NA NA NA NA NA ...##The Histogram for all correct responses:
library(ggplot2)
#Shows a frequency histogram of the response RTs
ggplot(Compiled, aes(x=RT)) + #Change rt to whatever the dependent variable is!
geom_histogram(breaks=seq(0, 2000, by=25))
NA
NA##Trial Classification
##3. Trial Classification
#########################
#File_Name should be in " "
# 1-error (just incorrect responses)
# 2-correct response within cutoff range
# 3-outlier (correct response just too slow, or no response)
Trial_classification <- function(input_file,File_Name, time_out, lower_RT, upper_RT){
input_file$Trial_Criterion[input_file$RT < 0 & input_file$RT > time_out] <- 1 #error
input_file$Trial_Criterion[input_file$RT > lower_RT & input_file$RT < upper_RT] <- 2 #correct response
input_file$Trial_Criterion[input_file$RT == time_out |
(input_file$RT < lower_RT & input_file$RT > 0)|
input_file$RT > upper_RT] <- 3 #outliers and no response
assign(File_Name, input_file, envir = .GlobalEnv)
return(str(input_file[1:10,]))
}
Trial_classification(Compiled,"Compiled",-2500,250,1750)'data.frame': 10 obs. of 21 variables:
$ Computer : Factor w/ 6 levels "1","2","3","BA",..: 6 6 6 6 6 6 6 6 6 6
$ Group : Factor w/ 4 levels "L1","L2","L3",..: 1 1 1 1 1 1 1 1 1 1
$ Count : int 1 2 3 4 5 6 7 8 9 10
$ Subject : int 1 1 1 1 1 1 1 1 1 1
$ RefreshRate : Factor w/ 3 levels "16.58ms","16.62ms",..: 3 3 3 3 3 3 3 3 3 3
$ Item : int 705 706 707 708 701 712 702 703 704 709
$ RT : num 717 748 -649 611 622 ...
$ Target : Factor w/ 140 levels "ACHE","BEAL",..: 140 38 101 100 27 34 109 120 115 54
$ List : int 0 0 0 0 0 0 0 0 0 0
$ Lexicality : Factor w/ 3 levels "nonword","practice",..: 2 2 2 2 2 2 2 2 2 2
$ OrthPrime : Factor w/ 199 levels "achb","baqt",..: 156 124 16 131 125 171 162 164 146 73
$ Orth_Rel : Factor w/ 3 levels "practice","related",..: 1 1 1 1 1 1 1 1 1 1
$ SemPrime : Factor w/ 203 levels "adorable","all",..: 135 84 61 118 5 86 23 66 76 13
$ Sem_Rel : Factor w/ 4 levels "NONE","practice",..: 2 2 2 2 2 2 2 2 2 2
$ Trial.Type : Factor w/ 3 levels "correct","incorrect",..: 3 3 3 3 3 3 3 3 3 3
$ X : logi NA NA NA NA NA NA ...
$ X.1 : logi NA NA NA NA NA NA ...
$ X.2 : logi NA NA NA NA NA NA ...
$ X.3 : logi NA NA NA NA NA NA ...
$ X.4 : logi NA NA NA NA NA NA ...
$ Trial_Criterion: num 2 2 1 2 2 2 2 2 2 2#When the variable information is generated, recode the variable classes as necessary
#Recode the column variable types as necessary
cols <- c("Subject", "Item", "Trial_Criterion", "List")
Compiled[,cols] <- lapply(Compiled[,cols], as.factor)
#Get rid of any empty columns
Compiled <-Compiled[, -grep("^X", colnames(Compiled))]
str(Compiled[1:10,])'data.frame': 10 obs. of 16 variables:
$ Computer : Factor w/ 6 levels "1","2","3","BA",..: 6 6 6 6 6 6 6 6 6 6
$ Group : Factor w/ 4 levels "L1","L2","L3",..: 1 1 1 1 1 1 1 1 1 1
$ Count : int 1 2 3 4 5 6 7 8 9 10
$ Subject : Factor w/ 85 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1
$ RefreshRate : Factor w/ 3 levels "16.58ms","16.62ms",..: 3 3 3 3 3 3 3 3 3 3
$ Item : Factor w/ 140 levels "1","2","3","4",..: 133 134 135 136 129 140 130 131 132 137
$ RT : num 717 748 -649 611 622 ...
$ Target : Factor w/ 140 levels "ACHE","BEAL",..: 140 38 101 100 27 34 109 120 115 54
$ List : Factor w/ 5 levels "0","1","2","3",..: 1 1 1 1 1 1 1 1 1 1
$ Lexicality : Factor w/ 3 levels "nonword","practice",..: 2 2 2 2 2 2 2 2 2 2
$ OrthPrime : Factor w/ 199 levels "achb","baqt",..: 156 124 16 131 125 171 162 164 146 73
$ Orth_Rel : Factor w/ 3 levels "practice","related",..: 1 1 1 1 1 1 1 1 1 1
$ SemPrime : Factor w/ 203 levels "adorable","all",..: 135 84 61 118 5 86 23 66 76 13
$ Sem_Rel : Factor w/ 4 levels "NONE","practice",..: 2 2 2 2 2 2 2 2 2 2
$ Trial.Type : Factor w/ 3 levels "correct","incorrect",..: 3 3 3 3 3 3 3 3 3 3
$ Trial_Criterion: Factor w/ 3 levels "1","2","3": 2 2 1 2 2 2 2 2 2 2#Pivot Table with summary of subject errors
library(rpivotTable)
subject_errors<- rpivotTable(data = Compiled,
rows = c("Group","Subject"), #list was counterbalance
cols = c("Lexicality","Trial_Criterion"),
vals = "Trial_Criterion",
aggregatorName = "Count",
rendererName = "Error Rates",
width="100%",
height="100px"
)
print(subject_errors)
NULL#Pivot Table with summary of item errors
library(rpivotTable)
item_errors<- rpivotTable(data = Compiled,
rows = c("Lexicality","Target"), #list was counterbalance
cols = c("Trial_Criterion"),
vals = "Trial_Criterion",
aggregatorName = "Count",
rendererName = "Error Rates",
width="100%",
height="100px"
)
print(item_errors)
NULL##Error Rates Prior to Subject and Trial Removal##Removing Subjects, Items and Trials
##Removing Subjects and Trials
#################################
#Removes a list of specified subjects
#File_Name should be in " "
Trial_Cleaning <- function(input_file,Clean_File,Error_File,PreAnalysis_File,...){
removed_SubjectsORItems <- list(...) #list of subjects to remove
tmp1 <- input_file[ ! input_file$Subject %in% removed_SubjectsORItems, ] #removing the subjects
tmp2 <- tmp1[ ! tmp1$Target %in% removed_SubjectsORItems,] #Revmoing the items
practice_trials <- which(with(tmp2, Lexicality == "practice")) #Defining Practice Trials
tmp3 <- tmp2[-practice_trials,] #removing the practice trials
assign(PreAnalysis_File, tmp3, envir = .GlobalEnv) #File with bad subjects and items removed but with outliers and errors present. This is for counting the number of error and outlier trials
outlier_trials <- which(with(tmp3, Trial_Criterion == 3)) #Defining Outlier Trials
tmp4 <- tmp3[-outlier_trials,] #Revmoing the outlier trials
assign(Error_File, tmp4, envir = .GlobalEnv) #Error file has incorrect and correct responses
error_trials <- which(with(tmp4, Trial_Criterion == 1)) #list of rows to remove
tmp5 <- tmp4[-error_trials,] #Removing Error Trials
assign(Clean_File, tmp5, envir = .GlobalEnv)
subject_prelim <- aggregate(RT ~ Group + Subject, data=tmp5, mean, na.rm=TRUE)
return(subject_prelim)
}
Trial_Cleaning(Compiled,"Clean","Errors","PreAnalysis", 2,7,9,101,12,1038,21,22,1007,1011,1004,1008,1016)#Results Summary
##Creating Variables of Interest to summarize
##############
#SUMMARY_STATS
##############
#Basic Ex-Gaussian Functions
mux <- function(x, n = length(x)) { # This gives me only the mu
k <- start <- c(mu = NaN, sigma = NaN, tau = NaN)
k[1] <- mean(x)
xdev <- x - k[1]
k[2] <- sum(xdev^2)/(n-1)
k[3] <- sum(xdev^3)/(n-1)
if (k[3] > 0)
start[3] <- (k[3]/2)^(1/3)
else start[3] <- 0.8*sd(x)
start[2] <- sqrt(abs(k[2] - start[3]^2))
start[1] <- k[1] - start[3]
return(start[1])
}
sigx <- function(x, n = length(x)) { # THIS GIVES ME ONLY THE SIGMA
k <- start <- c(mu = NaN, sigma = NaN, tau = NaN)
k[1] <- mean(x)
xdev <- x - k[1]
k[2] <- sum(xdev^2)/(n-1)
k[3] <- sum(xdev^3)/(n-1)
if (k[3] > 0)
start[3] <- (k[3]/2)^(1/3)
else start[3] <- 0.8*sd(x)
start[2] <- sqrt(abs(k[2] - start[3]^2))
start[1] <- k[1] - start[3]
return(start[2])
}
taux <- function(x, n = length(x)) { # THIS GIVES ME ONLY THE TAU
k <- start <- c(mu = NaN, sigma = NaN, tau = NaN)
k[1] <- mean(x)
xdev <- x - k[1]
k[2] <- sum(xdev^2)/(n-1)
k[3] <- sum(xdev^3)/(n-1)
if (k[3] > 0)
start[3] <- (k[3]/2)^(1/3)
else start[3] <- 0.8*sd(x)
start[2] <- sqrt(abs(k[2] - start[3]^2))
start[1] <- k[1] - start[3]
return(start[3])
}
#Other Functions
SE <- function(x){se = sd(x)/sqrt(length(x))}
ErrorRate <- function(x){
ER = length(which(x == 1))/length(which(x == 1| x ==2))
}
##Creating Data Frame Summaries Subject and item summaries for words and errors
#library(data.table)
#Summary Definition Protocols
Subject_RT <- list(Clean$Subject, Clean$Group, Clean$Sem_Rel, Clean$Orth_Rel, Clean$Lexicality)
Subject_Errors <- list (Errors$Subject, Errors$Group, Errors$Sem_Rel, Errors$Orth_Rel, Errors$Lexicality)
Item_RT <- list(Clean$Target, Clean$List, Clean$Sem_Rel, Clean$Orth_Rel, Clean$Lexicality)
Item_Errors <- list(Errors$Target, Errors$List, Errors$Sem_Rel, Errors$Orth_Rel, Errors$Lexicality)
##Subject means
Subject <- aggregate(RT ~ Subject + Group + Sem_Rel + Orth_Rel + Lexicality, data = Clean, FUN = mean, na.rm = TRUE) #RT Means
Subject$SE <- na.omit(sapply(split(Clean$RT, Subject_RT), SE)) #SE
Subject$Mu <- na.omit(sapply(split(Clean$RT, Subject_RT), mux)) #Mu
Subject$Sigma <- na.omit(sapply(split(Clean$RT, Subject_RT), sigx)) #Sigma
Subject$Tau <- na.omit(sapply(split(Clean$RT, Subject_RT), taux)) #Tau
Subject$ErrorRate <- na.omit(sapply(split(Errors$Trial_Criterion, Subject_Errors), ErrorRate)) #Error Rate
##Item means
Item <- aggregate(RT ~ Target + List + Sem_Rel + Orth_Rel + Lexicality, data = Clean, FUN = mean, na.rm = TRUE) #RT Means
Item$SE <- na.omit(sapply(split(Clean$RT, Item_RT), SE)) #SE
Item$Mu <- na.omit(sapply(split(Clean$RT, Item_RT), mux)) #Mu
Item$Sigma <- na.omit(sapply(split(Clean$RT, Item_RT), sigx)) #Sigma
Item$Tau <- na.omit(sapply(split(Clean$RT, Item_RT), taux)) #Tau
Item$ErrorRate <- na.omit(sapply(split(Errors$Trial_Criterion, Item_Errors), ErrorRate)) #Error Rate
#Condition-level means
Conditions <- aggregate(cbind(RT, SE, Mu, Sigma, Tau, ErrorRate) ~ Sem_Rel + Orth_Rel + Lexicality, data = Subject, FUN = mean)
print(Conditions)##Splitting Latency and Error files into Words and Non-words
##Splitting File
###################
#Always split words/nonwords firts!!!
#File_Name1/2, subset1/2, and criterion_variable should be in " "
File_Split <- function(input_file, criterion_variable, File_Name1,subset1, File_Name2,subset2){
tmp1 <- input_file[input_file[criterion_variable]==subset1,]
assign(File_Name1, tmp1, envir = .GlobalEnv)
tmp2 <- input_file[input_file[criterion_variable]==subset2,]
assign(File_Name2, tmp2, envir = .GlobalEnv)
}
##Subject and Item Latency and Error Analyses
#Data Frames for Subject Latency and Error Summaries
File_Split(Subject, "Lexicality", "Word_Subject", "word", "Nonword_Subject", "nonword")
#Data frames for Item Latency and Error Summaries
File_Split(Item, "Lexicality", "Word_Item", "word", "Nonword_Item", "nonword")
##Trial Level Data for GLMER analyses and Quartile Analyses
#Clean Datafrmaes
File_Split(Clean, "Lexicality", "WordRT_Trial", "word", "NonwordRT_Trial", "nonword")
#Error Dataframes
File_Split(Errors, "Lexicality", "WordErrors_Trial", "word", "NonwordErrors_Trial", "nonword")
#======== #Analyses #========
##ANOVAs
###Latencies
library(afex)
library(apa)
library(emmeans)
#Word Subject-Level ANOVA
FsW <-aov_ez("Subject", #Subject variable
"RT", #dependent variable
Word_Subject, #dataset
between = c("Group"),
within = c("Sem_Rel","Orth_Rel"),
#afex_options(correction_aov = "none"), #Correction of d.f.!!!
type = 3,
factorize = TRUE)
FsW_omnibus <- anova(FsW)
print("Word Subject-Level ANOVA")
print(FsW_omnibus)
anova_apa(FsW)
mFsW <- emmeans(FsW,~Sem_Rel|Orth_Rel)
mFsW
pairs(mFsW)
#Word Item-Level ANOVA
FiW <-aov_ez("Target", #Subject variable
"RT", #dependent variable
Word_Item, #dataset
between = c("List"),
within = c("Sem_Rel","Orth_Rel"),
#afex_options(correction_aov = "none"), #Correction of d.f.!!!
type = 3,
factorize = TRUE)
FiW_omnibus <- anova(FiW)
print("Word Item-Level ANOVA")
print(FiW_omnibus)
anova_apa(FiW)
#Nonword Subject-Level ANOVA
FsNw <-aov_ez("Subject", #Subject variable
"RT", #dependent variable
Nonword_Subject, #dataset
between = c("Group"),
within = c("Orth_Rel"),
#afex_options(correction_aov = "none"), #Correction of d.f.!!!
type = 3,
factorize = TRUE)
FsNw_omnibus <- anova(FsNw)
print("Nonword Subject-Level ANOVA")
print(FsNw_omnibus)
anova_apa(FsNw)
#Nonword Item-Level ANOVA
FiNw <-aov_ez("Target", #Subject variable
"RT", #dependent variable
Nonword_Item, #dataset
between = c("List"),
within = c("Orth_Rel"),
#afex_options(correction_aov = "none"), #Correction of d.f.!!!
type = 3,
factorize = TRUE)
FiNw_omnibus <- anova(FiNw)
print("Nonword Subject-Level ANOVA")
print(FiNw_omnibus)
anova_apa(FiNw)
###Errors
library(afex)
library(apa)
library(emmeans)
#Word Subject-Level ANOVA
FsW <-aov_ez("Subject", #Subject variable
"ErrorRate", #dependent variable
Word_Subject, #dataset
between = c("Group"),
within = c("Sem_Rel","Orth_Rel"),
#afex_options(correction_aov = "none"), #Correction of d.f.!!!
type = 3,
factorize = TRUE)
FsW_omnibus <- anova(FsW)
print("Word Subject-Level ANOVA")
print(FsW_omnibus)
anova_apa(FsW)
mFsW <- emmeans(FsW,~Orth_Rel|Sem_Rel)
mFsW
pairs(mFsW)
#Word Item-Level ANOVA
FiW <-aov_ez("Target", #Subject variable
"ErrorRate", #dependent variable
Word_Item, #dataset
between = c("List"),
within = c("Sem_Rel","Orth_Rel"),
#afex_options(correction_aov = "none"), #Correction of d.f.!!!
type = 3,
factorize = TRUE)
FiW_omnibus <- anova(FiW)
print("Word Item-Level ANOVA")
print(FiW_omnibus)
anova_apa(FiW)
#Nonword Subject-Level ANOVA
FsNw <-aov_ez("Subject", #Subject variable
"ErrorRate", #dependent variable
Nonword_Subject, #dataset
between = c("Group"),
within = c("Orth_Rel"),
#afex_options(correction_aov = "none"), #Correction of d.f.!!!
type = 3,
factorize = TRUE)
FsNw_omnibus <- anova(FsNw)
print("Nonword Subject-Level ANOVA")
print(FsNw_omnibus)
anova_apa(FsNw)
#Nonword Item-Level ANOVA
FiNw <-aov_ez("Target", #Subject variable
"ErrorRate", #dependent variable
Nonword_Item, #dataset
between = c("List"),
within = c("Orth_Rel"),
#afex_options(correction_aov = "none"), #Correction of d.f.!!!
type = 3,
factorize = TRUE)
FiNw_omnibus <- anova(FiNw)
print("Nonword Subject-Level ANOVA")
print(FiNw_omnibus)
anova_apa(FiNw)
##GLME
###Latencies
#TROUBLESHOOTING: check out this page: http://rpubs.com/bbolker/lme4trouble1
library(lme4)
library(car)
options(contrasts = c("contr.sum","contr.poly"))
#Inverse Gaussian Distribution
wordRT_invgauss <- glmer(RT ~ Orth_Rel +
(1|Target) + (1|Subject),
data = WordRT_Trial,
family = inverse.gaussian(link="identity"))
summary(wordRT_invgauss)
#Anova(wordRT_invgauss)
#restart from previous fit
#ss <- getME(wordRT_invgauss,c("theta","fixef"))
#wordRT_invgauss2 <- update(wordRT_invgauss,start=ss,control=glmerControl(optCtrl=list(maxfun=1e9)))
#summary(wordRT_invgauss2)
###Errors
library(lme4)
options(contrasts = c("contr.sum","contr.poly"))
#Obtaining the maximal model (This is the most complex model which could still converge)
wordError_Binomial <- glmer(Trial_Criterion ~ Orth_Rel + #Fixed effects and their interactions
(1|Subject) + (1|Target), #Random effects with random intercepts
data = WordErrors_Trial,
family = "binomial",
nAGO = 1
)
summary(wordError_Binomial)
#================= #Quantile Analyses #=================
##Setting up Quartile Analyses
Quantile_Assign <- function(input_file, File_Name, Var, QuantileLabel, QuantileSize, MaxRank){
input_file$rank <- ave(input_file$RT, input_file$Subject, input_file[,Var], FUN = rank)
input_file$rank <- as.integer(input_file$rank)
i <- 1
while(i*QuantileSize < MaxRank){
input_file$tmp[input_file$rank >=(i-1)*QuantileSize + 1 & input_file$rank <= QuantileSize*i] <- i
i <- i + 1
}
input_file[ ,QuantileLabel] <- input_file$tmp
input_file <- input_file[ , !names(input_file) %in% c("tmp","rank")]
assign(File_Name, input_file, envir = .GlobalEnv)
}
WordErrors_TrialQ <- WordErrors_Trial
WordRT_TrialQ<- WordRT_Trial
Quantile_Assign(WordRT_TrialQ,"WordRT_TrialQ", "Orth_Rel", "OrthRelQ", 7, 36)
Quantile_Assign(WordErrors_TrialQ,"WordErrors_TrialQ", "Orth_Rel", "OrthRelQ", 7, 36)
Quantile_Assign(WordRT_TrialQ,"WordRT_TrialQ", "Sem_Rel", "SemRelQ", 7, 36)
Quantile_Assign(WordErrors_TrialQ,"WordErrors_TrialQ", "Sem_Rel", "SemRelQ", 7, 36)
WordRT_Trial_OrthRelQ <- subset(WordRT_TrialQ, OrthRelQ >=1 & OrthRelQ <=4)
WordRT_Trial_SemRelQ <- subset(WordRT_TrialQ, SemRelQ >=1 & SemRelQ <=4)
##ANOVAs
###Latencies
library(afex)
library(emmeans)
library(apa)
##OrthRelQ Latencies
####################
#Subject Analysis
FsOrthQ <-aov_ez("Subject", #Subject variable
"RT", #dependent variable
WordRT_Trial_OrthRelQ, #dataset
within = c("Orth_Rel","OrthRelQ"),
between = c("Group"),
correction = "none", #Correction of d.f.!!!
type = 3,
factorize = TRUE)
print("Subject-Level ANOVA")
print(anova(FsOrthQ))
anova_apa(FsOrthQ)
MsOrthQ <- emmeans(FsOrthQ, ~ Orth_Rel|OrthRelQ)
#Item Analysis
FiOrthQ <-aov_ez("Target", #Subject variable
"RT", #dependent variable
WordRT_Trial_OrthRelQ, #dataset
within = c("Orth_Rel","OrthRelQ"),
between = c("List"),
correction = "none", #Correction of d.f.!!!
type = 3,
factorize = TRUE)
print("Item-Level ANOVA")
print(anova(FiOrthQ))
anova_apa(FiOrthQ)
MsOrthQ <- emmeans(FiOrthQ, ~ Orth_Rel|OrthRelQ)
##SemRelQ Latencies
####################
#Subject Analysis
FsSemQ <-aov_ez("Subject", #Subject variable
"RT", #dependent variable
WordRT_Trial_SemRelQ, #dataset
within = c("Sem_Rel","SemRelQ"),
between = c("Group"),
correction = "none", #Correction of d.f.!!!
type = 3,
factorize = TRUE)
print("Subject-Level ANOVA")
print(anova(FsSemQ))
anova_apa(FsSemQ)
MsOrthQ <- emmeans(FsSemQ, ~ Sem_Rel|SemRelQ)
#Item Analysis
FiSemQ <-aov_ez("Target", #Subject variable
"RT", #dependent variable
WordRT_Trial_SemRelQ, #dataset
within = c("Sem_Rel","SemRelQ"),
between = c("List"),
correction = "none", #Correction of d.f.!!!
type = 3,
factorize = TRUE)
print("Item-Level ANOVA")
print(anova(FiSemQ))
anova_apa(FiSemQ)
MsOrthQ <- emmeans(FiSemQ, ~ Sem_Rel|SemRelQ)
library(ggplot2)
library(ggrepel)
library(gridExtra)
OrthRelPlot <- emmip(FsOrthQ, Orth_Rel ~ OrthRelQ, CIs = TRUE, engine = "ggplot", plotit = FALSE)
OrthRelPlot$tvar <- as.factor(OrthRelPlot$tvar)
levels(OrthRelPlot$tvar) <- c(levels(OrthRelPlot$tvar), "Neighbor")
levels(OrthRelPlot$tvar) <- c(levels(OrthRelPlot$tvar), "Non-neighbor")
OrthRelPlot[OrthRelPlot == "related"] <- "Neighbor"
OrthRelPlot[OrthRelPlot == "unrelated"] <- "Non-neighbor"
print(OrthRelPlot)
SemRelPlot <- emmip(FsSemQ, Sem_Rel ~ SemRelQ, CIs = TRUE, engine = "ggplot", plotit = FALSE)
SemRelPlot$tvar <- as.factor(SemRelPlot$tvar)
levels(SemRelPlot$tvar) <- c(levels(SemRelPlot$tvar), "Related")
levels(SemRelPlot$tvar) <- c(levels(SemRelPlot$tvar), "Unrelated")
SemRelPlot[SemRelPlot == "related"] <- "Related"
SemRelPlot[SemRelPlot == "unrelated"] <- "Unrelated"
print(SemRelPlot)
Theme_Add <- theme_bw(base_size = 12, base_family = "") +
theme(plot.title = element_text(color = "black",size=12)) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),panel.grid.minor = element_blank()) +
theme(axis.line = element_line(colour = "black", linetype = "solid")) +
theme(legend.justification=c(1,0),legend.position=c(1,0.05),
legend.title=element_blank(),
legend.text=element_text(colour="black", size=12),
legend.key = element_rect((fill = NA))) +
theme(axis.text.x = element_text(color = "black",size=12),
axis.text.y = element_text(color = "black",size=12))
QuartileLabels <- c("1", "2", "3", "4" )
OrthRelQ_Plot <- ggplot(data = OrthRelPlot, aes(x = xvar, y=yvar, group = tvar))+
geom_line(aes(linetype=tvar), size=1) +
geom_point() +
geom_errorbar(aes(ymin=LCL, ymax=UCL), width=.1) +
geom_text_repel(aes(label=format(OrthRelPlot$yvar, digits=3)),
size = 4,direction = "y", hjust = 2, segment.colour = NA) +
labs(title = "Orthographic Priming Effect", x = "Quantile", y = "RT (ms)") +
scale_x_discrete(limits = c("X1", "X2", "X3", "X4"),labels= QuartileLabels) +
Theme_Add
print(OrthRelQ_Plot)
SemRelQ_Plot <- ggplot(data = SemRelPlot, aes(x = xvar, y=yvar, group = tvar))+
geom_line(aes(linetype=tvar), size=1) +
geom_point() +
geom_errorbar(aes(ymin=LCL, ymax=UCL), width=.1) +
geom_text_repel(aes(label=format(SemRelPlot$yvar, digits=3)),
size = 4, direction = "y", hjust = 2, segment.colour = NA) +
labs(title = "Semantic Priming Effect", x = "Quantile", y = "RT (ms)") +
scale_x_discrete(limits = c("X1", "X2", "X3", "X4"),labels= QuartileLabels) +
Theme_Add
print(SemRelQ_Plot)
ggsave("C:/Users/Alexander/Desktop/MyPlots/Exp1LongSOA_OrthRel.png", plot = OrthRelQ_Plot,scale = 1, width = 16, height = 9, units = c("cm"), dpi = 300, limitsize = TRUE)
ggsave("C:/Users/Alexander/Desktop/MyPlots/Exp1LongSOA_SemRel.png", plot = SemRelQ_Plot,scale = 1, width = 16, height = 9, units = c("cm"), dpi = 300, limitsize = TRUE)
#pdf("C:/Users/Alexander/Desktop/MyPlots/S64_LongSOA_OrthFacil.pdf", width = 12, height = 8)
#S64_LongSOA_OrthFacil <- grid.arrange(SemRelQ_Plot,OrthRelQ_Plot, ncol=2, nrow=1)
#dev.off() # Close the file
---
title: "S64 Long SOA Sem Pri Orthographic Facilitation"
output:
  word_document: default
  html_notebook: default
  pdf_document: default
  html_document: default
---


#Pre-Processing

##Importing and examining the trial-level dataset
```{r Importing_and_examining}
##Import
###########
#Imports the data file and saves it into the global environment.
#Gets rid of any extra columns with an X
#Directory and File_Name should be in " " 

#directory is "C:/Users/Alexander/Desktop/OrthFacil/RCode/"
Importing_and_examining <- function(directory, File_Name){
  setwd(directory) #set working directory to folder with files
  experiment <- list.files(pattern="*.txt") #Get name of the experiment data file
  filepath <- file.path(directory, experiment) #Get filepath
  Compiled <- read.delim(filepath, header =TRUE) #Import file specified by filepath
  assign(File_Name, Compiled, envir = .GlobalEnv)
  return(str(Compiled[1:10,]))
}

Importing_and_examining("C:/Users/Alexander/Desktop/S64_LongSOASemPri_OrthFacil/SemPri_OrthFacil_RCode", "Compiled")

```


##The Histogram for all correct responses:
```{r RT_distribution}
library(ggplot2)
#Shows a frequency histogram of the response RTs
ggplot(Compiled, aes(x=RT)) + #Change rt to whatever the dependent variable is!
           geom_histogram(breaks=seq(0, 2000, by=25))


```

##Trial Classification
```{r Trial_Classification}
##3. Trial Classification
#########################
#File_Name should be in " "
# 1-error (just incorrect responses)
# 2-correct response within cutoff range
# 3-outlier (correct response just too slow, or no response) 
Trial_classification <- function(input_file,File_Name, time_out, lower_RT, upper_RT){
  input_file$Trial_Criterion[input_file$RT < 0 & input_file$RT > time_out] <- 1 #error
  input_file$Trial_Criterion[input_file$RT > lower_RT & input_file$RT < upper_RT] <- 2 #correct response
  input_file$Trial_Criterion[input_file$RT == time_out | 
                               (input_file$RT < lower_RT & input_file$RT > 0)|
                               input_file$RT > upper_RT] <- 3 #outliers and no response
  assign(File_Name, input_file, envir = .GlobalEnv)
  return(str(input_file[1:10,]))
}
Trial_classification(Compiled,"Compiled",-2500,250,1750)
#When the variable information is generated, recode the variable classes as necessary

```

```{r Correcting Columns}
#Recode the column variable types as necessary
cols <- c("Subject", "Item", "Trial_Criterion", "List")
Compiled[,cols] <- lapply(Compiled[,cols], as.factor)

#Get rid of any empty columns
Compiled <-Compiled[, -grep("^X", colnames(Compiled))] 
str(Compiled[1:10,])
```

#Pivot Table with summary of subject errors
```{r Pivot Table Summary of Subject Errors}
library(rpivotTable)
subject_errors<- rpivotTable(data = Compiled,
                      rows = c("Group","Subject"), #list was counterbalance
                      cols = c("Lexicality","Trial_Criterion"),  
                      vals = "Trial_Criterion",
                      aggregatorName = "Count",
                      rendererName = "Error Rates",
                      width="100%", 
                      height="100px"
  )
print(subject_errors)
```

#Pivot Table with summary of item errors
```{r Pivot Table Summary of Item Errors}
library(rpivotTable)
item_errors<- rpivotTable(data = Compiled,
                      rows = c("Lexicality","Target"), #list was counterbalance
                      cols = c("Trial_Criterion"),  
                      vals = "Trial_Criterion",
                      aggregatorName = "Count",
                      rendererName = "Error Rates",
                      width="100%", 
                      height="100px"
  )
print(item_errors)

##Error Rates Prior to Subject and Trial Removal
```


##Removing Subjects, Items and Trials
```{r Trial_Cleaning}
##Removing Subjects and Trials
#################################
#Removes a list of specified subjects 
#File_Name should be in " "
Trial_Cleaning <- function(input_file,Clean_File,Error_File,PreAnalysis_File,...){
  removed_SubjectsORItems <- list(...) #list of subjects to remove
  tmp1 <- input_file[ ! input_file$Subject %in% removed_SubjectsORItems, ] #removing the subjects
  tmp2 <- tmp1[ ! tmp1$Target %in% removed_SubjectsORItems,] #Revmoing the items
  practice_trials <- which(with(tmp2, Lexicality == "practice")) #Defining Practice Trials
  tmp3 <- tmp2[-practice_trials,] #removing the practice trials
  assign(PreAnalysis_File, tmp3, envir = .GlobalEnv) #File with bad subjects and items removed but with outliers and errors present. This is for counting the number of error and outlier trials
  outlier_trials <- which(with(tmp3, Trial_Criterion == 3)) #Defining Outlier Trials
  tmp4 <- tmp3[-outlier_trials,] #Revmoing the outlier trials
  assign(Error_File, tmp4, envir = .GlobalEnv) #Error file has incorrect and correct responses
  error_trials <- which(with(tmp4, Trial_Criterion == 1)) #list of rows to remove
  tmp5 <- tmp4[-error_trials,] #Removing Error Trials
  assign(Clean_File, tmp5, envir = .GlobalEnv)
  subject_prelim <- aggregate(RT ~ Group + Subject, data=tmp5, mean, na.rm=TRUE)
  return(subject_prelim)
}
Trial_Cleaning(Compiled,"Clean","Errors","PreAnalysis", 2,7,9,101,12,1038,21,22,1007,1011,1004,1008,1016)
```

```{r, eval=FALSE, include=FALSE}
##Writing Intermediate File
##############################
#Writes file with cleaned data
#Directory is "C:/Users/Alexander/Desktop/Intermediates/"
#File_Name should include a .txt
Clean_Write <- function(input_file, File_Name, directory){
  filepath <- file.path(directory, File_Name)
  write.table(input_file, filepath, sep="\t")
  
}
```


#Results Summary

##Creating Variables of Interest to summarize
```{r Summary Variables}
##############
#SUMMARY_STATS
##############

#Basic Ex-Gaussian Functions
mux <- function(x, n = length(x)) { # This gives me only the mu
	k <- start <- c(mu = NaN, sigma = NaN, tau = NaN)
	k[1] <- mean(x)
	xdev <- x - k[1]
	k[2] <- sum(xdev^2)/(n-1)
	k[3] <- sum(xdev^3)/(n-1)
	if (k[3] > 0)
		start[3] <- (k[3]/2)^(1/3)
	else start[3] <- 0.8*sd(x)
	start[2] <- sqrt(abs(k[2] - start[3]^2))
	start[1] <- k[1] - start[3]
	return(start[1])
}

sigx <- function(x, n = length(x)) { # THIS GIVES ME ONLY THE SIGMA
	k <- start <- c(mu = NaN, sigma = NaN, tau = NaN)
	k[1] <- mean(x)
	xdev <- x - k[1]
	k[2] <- sum(xdev^2)/(n-1)
	k[3] <- sum(xdev^3)/(n-1)
	if (k[3] > 0)
		start[3] <- (k[3]/2)^(1/3)
	else start[3] <- 0.8*sd(x)
	start[2] <- sqrt(abs(k[2] - start[3]^2))
	start[1] <- k[1] - start[3]
	return(start[2])
}

taux <- function(x, n = length(x)) { # THIS GIVES ME ONLY THE TAU
	k <- start <- c(mu = NaN, sigma = NaN, tau = NaN)
	k[1] <- mean(x)
	xdev <- x - k[1]
	k[2] <- sum(xdev^2)/(n-1)
	k[3] <- sum(xdev^3)/(n-1)
	if (k[3] > 0)
		start[3] <- (k[3]/2)^(1/3)
	else start[3] <- 0.8*sd(x)
	start[2] <- sqrt(abs(k[2] - start[3]^2))
	start[1] <- k[1] - start[3]
	return(start[3])
}

#Other Functions

SE <- function(x){se = sd(x)/sqrt(length(x))}

ErrorRate <- function(x){
  ER = length(which(x == 1))/length(which(x == 1| x ==2))
}

```

##Creating Data Frame Summaries
Subject and item summaries for words and errors
```{r Summary Functions}
#library(data.table)
#Summary Definition Protocols

Subject_RT <- list(Clean$Subject, Clean$Group, Clean$Sem_Rel, Clean$Orth_Rel, Clean$Lexicality)
Subject_Errors <- list (Errors$Subject, Errors$Group, Errors$Sem_Rel, Errors$Orth_Rel, Errors$Lexicality)

Item_RT <- list(Clean$Target, Clean$List, Clean$Sem_Rel, Clean$Orth_Rel, Clean$Lexicality)
Item_Errors <- list(Errors$Target, Errors$List, Errors$Sem_Rel, Errors$Orth_Rel, Errors$Lexicality)


##Subject means
Subject <- aggregate(RT ~ Subject + Group + Sem_Rel + Orth_Rel + Lexicality, data = Clean, FUN = mean, na.rm = TRUE) #RT Means
Subject$SE <- na.omit(sapply(split(Clean$RT, Subject_RT), SE)) #SE
Subject$Mu <- na.omit(sapply(split(Clean$RT, Subject_RT), mux)) #Mu 
Subject$Sigma <- na.omit(sapply(split(Clean$RT, Subject_RT), sigx)) #Sigma
Subject$Tau <- na.omit(sapply(split(Clean$RT, Subject_RT), taux)) #Tau
Subject$ErrorRate <- na.omit(sapply(split(Errors$Trial_Criterion, Subject_Errors), ErrorRate)) #Error Rate

##Item means
Item <- aggregate(RT ~ Target + List + Sem_Rel + Orth_Rel + Lexicality, data = Clean, FUN = mean, na.rm = TRUE) #RT Means
Item$SE <- na.omit(sapply(split(Clean$RT, Item_RT), SE)) #SE
Item$Mu <- na.omit(sapply(split(Clean$RT, Item_RT), mux)) #Mu 
Item$Sigma <- na.omit(sapply(split(Clean$RT, Item_RT), sigx)) #Sigma
Item$Tau <- na.omit(sapply(split(Clean$RT, Item_RT), taux)) #Tau
Item$ErrorRate <- na.omit(sapply(split(Errors$Trial_Criterion, Item_Errors), ErrorRate)) #Error Rate

#Condition-level means
Conditions <- aggregate(cbind(RT, SE, Mu, Sigma, Tau, ErrorRate) ~ Sem_Rel + Orth_Rel + Lexicality, data = Subject, FUN = mean)
print(Conditions)
```

##Splitting Latency and Error files into Words and Non-words
```{r File_Split}
##Splitting File 
###################
#Always split words/nonwords firts!!!
#File_Name1/2, subset1/2, and criterion_variable should be in " "

File_Split <- function(input_file, criterion_variable, File_Name1,subset1, File_Name2,subset2){
  tmp1 <- input_file[input_file[criterion_variable]==subset1,]
  assign(File_Name1, tmp1, envir = .GlobalEnv)
  tmp2 <- input_file[input_file[criterion_variable]==subset2,]
  assign(File_Name2, tmp2, envir = .GlobalEnv)
  
}

##Subject and Item Latency and Error Analyses
#Data Frames for Subject Latency and Error Summaries
File_Split(Subject, "Lexicality", "Word_Subject", "word", "Nonword_Subject", "nonword")
#Data frames for Item Latency and Error Summaries
File_Split(Item, "Lexicality", "Word_Item", "word", "Nonword_Item", "nonword")

##Trial Level Data for GLMER analyses and Quartile Analyses
#Clean Datafrmaes
File_Split(Clean, "Lexicality", "WordRT_Trial", "word", "NonwordRT_Trial", "nonword")
#Error Dataframes
File_Split(Errors, "Lexicality", "WordErrors_Trial", "word", "NonwordErrors_Trial", "nonword")


```


#========
#Analyses
#========

##ANOVAs

###Latencies
```{r latency ANOVAs}
library(afex)
library(apa)
library(emmeans)

#Word Subject-Level ANOVA

FsW <-aov_ez("Subject", #Subject variable 
            "RT", #dependent variable
            Word_Subject, #dataset
            between = c("Group"),
            within = c("Sem_Rel","Orth_Rel"),
            #afex_options(correction_aov = "none"), #Correction of d.f.!!!
            type = 3,
            factorize = TRUE)
FsW_omnibus <- anova(FsW)
print("Word Subject-Level ANOVA")
print(FsW_omnibus)
anova_apa(FsW)

mFsW <- emmeans(FsW,~Sem_Rel|Orth_Rel)
mFsW
pairs(mFsW)



#Word Item-Level ANOVA

FiW <-aov_ez("Target", #Subject variable 
            "RT", #dependent variable
            Word_Item, #dataset
            between = c("List"),
            within = c("Sem_Rel","Orth_Rel"),
            #afex_options(correction_aov = "none"), #Correction of d.f.!!!
            type = 3,
            factorize = TRUE)


FiW_omnibus <- anova(FiW)
print("Word Item-Level ANOVA")
print(FiW_omnibus)
anova_apa(FiW)

#Nonword Subject-Level ANOVA

FsNw <-aov_ez("Subject", #Subject variable 
            "RT", #dependent variable
            Nonword_Subject, #dataset
            between = c("Group"),
            within = c("Orth_Rel"),
            #afex_options(correction_aov = "none"), #Correction of d.f.!!!
            type = 3,
            factorize = TRUE)
FsNw_omnibus <- anova(FsNw)
print("Nonword Subject-Level ANOVA")
print(FsNw_omnibus)
anova_apa(FsNw)

#Nonword Item-Level ANOVA

FiNw <-aov_ez("Target", #Subject variable 
            "RT", #dependent variable
            Nonword_Item, #dataset
            between = c("List"),
            within = c("Orth_Rel"),
            #afex_options(correction_aov = "none"), #Correction of d.f.!!!
            type = 3,
            factorize = TRUE)


FiNw_omnibus <- anova(FiNw)
print("Nonword Subject-Level ANOVA")
print(FiNw_omnibus)
anova_apa(FiNw)

```

###Errors
```{r Error Rate ANOVAs}
library(afex)
library(apa)
library(emmeans)
#Word Subject-Level ANOVA

FsW <-aov_ez("Subject", #Subject variable 
            "ErrorRate", #dependent variable
            Word_Subject, #dataset
            between = c("Group"),
            within = c("Sem_Rel","Orth_Rel"),
            #afex_options(correction_aov = "none"), #Correction of d.f.!!!
            type = 3,
            factorize = TRUE)
FsW_omnibus <- anova(FsW)
print("Word Subject-Level ANOVA")
print(FsW_omnibus)
anova_apa(FsW)

mFsW <- emmeans(FsW,~Orth_Rel|Sem_Rel)
mFsW
pairs(mFsW)


#Word Item-Level ANOVA

FiW <-aov_ez("Target", #Subject variable 
            "ErrorRate", #dependent variable
            Word_Item, #dataset
            between = c("List"),
            within = c("Sem_Rel","Orth_Rel"),
            #afex_options(correction_aov = "none"), #Correction of d.f.!!!
            type = 3,
            factorize = TRUE)


FiW_omnibus <- anova(FiW)
print("Word Item-Level ANOVA")
print(FiW_omnibus)
anova_apa(FiW)

#Nonword Subject-Level ANOVA

FsNw <-aov_ez("Subject", #Subject variable 
            "ErrorRate", #dependent variable
            Nonword_Subject, #dataset
            between = c("Group"),
            within = c("Orth_Rel"),
            #afex_options(correction_aov = "none"), #Correction of d.f.!!!
            type = 3,
            factorize = TRUE)
FsNw_omnibus <- anova(FsNw)
print("Nonword Subject-Level ANOVA")
print(FsNw_omnibus)
anova_apa(FsNw)

#Nonword Item-Level ANOVA

FiNw <-aov_ez("Target", #Subject variable 
            "ErrorRate", #dependent variable
            Nonword_Item, #dataset
            between = c("List"),
            within = c("Orth_Rel"),
            #afex_options(correction_aov = "none"), #Correction of d.f.!!!
            type = 3,
            factorize = TRUE)


FiNw_omnibus <- anova(FiNw)
print("Nonword Subject-Level ANOVA")
print(FiNw_omnibus)
anova_apa(FiNw)

```

##GLME

###Latencies
```{r GLMER on Latencies}
#TROUBLESHOOTING: check out this page: http://rpubs.com/bbolker/lme4trouble1

library(lme4)
library(car)

options(contrasts = c("contr.sum","contr.poly"))


#Inverse Gaussian Distribution
wordRT_invgauss <- glmer(RT ~ Orth_Rel + 
                           (1|Target) + (1|Subject), 
                               data = WordRT_Trial, 
                               family = inverse.gaussian(link="identity"))
summary(wordRT_invgauss)
#Anova(wordRT_invgauss)


#restart from previous fit

#ss <- getME(wordRT_invgauss,c("theta","fixef"))
#wordRT_invgauss2 <- update(wordRT_invgauss,start=ss,control=glmerControl(optCtrl=list(maxfun=1e9)))
#summary(wordRT_invgauss2)


```

###Errors 

```{r echo=TRUE}
library(lme4)
options(contrasts = c("contr.sum","contr.poly"))

#Obtaining the maximal model (This is the most complex model which could still converge)
wordError_Binomial <- glmer(Trial_Criterion ~ Orth_Rel +    #Fixed effects and their interactions
                   (1|Subject) + (1|Target), #Random effects with random intercepts   
                   data = WordErrors_Trial,
                   family = "binomial",
                   nAGO = 1
                   )
summary(wordError_Binomial)

```



#=================
#Quantile Analyses
#=================

##Setting up Quartile Analyses
```{r Quartile Assign}

Quantile_Assign <- function(input_file, File_Name, Var, QuantileLabel, QuantileSize, MaxRank){
  input_file$rank <- ave(input_file$RT, input_file$Subject, input_file[,Var], FUN = rank)
  input_file$rank <- as.integer(input_file$rank)
  i <- 1
  while(i*QuantileSize < MaxRank){
    input_file$tmp[input_file$rank >=(i-1)*QuantileSize + 1 & input_file$rank <= QuantileSize*i] <- i
    i <- i + 1
  }
  input_file[ ,QuantileLabel] <- input_file$tmp
  input_file <- input_file[ , !names(input_file) %in% c("tmp","rank")] 
  assign(File_Name, input_file, envir = .GlobalEnv)
}

WordErrors_TrialQ <- WordErrors_Trial
WordRT_TrialQ<- WordRT_Trial

Quantile_Assign(WordRT_TrialQ,"WordRT_TrialQ", "Orth_Rel", "OrthRelQ", 7, 36)
Quantile_Assign(WordErrors_TrialQ,"WordErrors_TrialQ", "Orth_Rel", "OrthRelQ", 7, 36)

Quantile_Assign(WordRT_TrialQ,"WordRT_TrialQ", "Sem_Rel", "SemRelQ", 7, 36)
Quantile_Assign(WordErrors_TrialQ,"WordErrors_TrialQ", "Sem_Rel", "SemRelQ", 7, 36)

WordRT_Trial_OrthRelQ <- subset(WordRT_TrialQ, OrthRelQ >=1 & OrthRelQ <=4)
WordRT_Trial_SemRelQ <- subset(WordRT_TrialQ, SemRelQ >=1 & SemRelQ <=4)

```

##ANOVAs

###Latencies
```{r Quartile Latency Analyses}
library(afex)
library(emmeans)
library(apa)

##OrthRelQ Latencies
####################

#Subject Analysis
FsOrthQ <-aov_ez("Subject", #Subject variable 
            "RT", #dependent variable
            WordRT_Trial_OrthRelQ, #dataset
            within = c("Orth_Rel","OrthRelQ"),
            between = c("Group"),
            correction = "none", #Correction of d.f.!!!
            type = 3,
            factorize = TRUE)
print("Subject-Level ANOVA")
print(anova(FsOrthQ))
anova_apa(FsOrthQ)
MsOrthQ <- emmeans(FsOrthQ, ~ Orth_Rel|OrthRelQ)

#Item Analysis
FiOrthQ <-aov_ez("Target", #Subject variable 
            "RT", #dependent variable
            WordRT_Trial_OrthRelQ, #dataset
            within = c("Orth_Rel","OrthRelQ"),
            between = c("List"),
            correction = "none", #Correction of d.f.!!!
            type = 3,
            factorize = TRUE)
print("Item-Level ANOVA")
print(anova(FiOrthQ))
anova_apa(FiOrthQ)
MsOrthQ <- emmeans(FiOrthQ, ~ Orth_Rel|OrthRelQ)

##SemRelQ Latencies
####################

#Subject Analysis
FsSemQ <-aov_ez("Subject", #Subject variable 
            "RT", #dependent variable
            WordRT_Trial_SemRelQ, #dataset
            within = c("Sem_Rel","SemRelQ"),
            between = c("Group"),
            correction = "none", #Correction of d.f.!!!
            type = 3,
            factorize = TRUE)
print("Subject-Level ANOVA")
print(anova(FsSemQ))
anova_apa(FsSemQ)
MsOrthQ <- emmeans(FsSemQ, ~ Sem_Rel|SemRelQ)

#Item Analysis
FiSemQ <-aov_ez("Target", #Subject variable 
            "RT", #dependent variable
            WordRT_Trial_SemRelQ, #dataset
            within = c("Sem_Rel","SemRelQ"),
            between = c("List"),
            correction = "none", #Correction of d.f.!!!
            type = 3,
            factorize = TRUE)
print("Item-Level ANOVA")
print(anova(FiSemQ))
anova_apa(FiSemQ)
MsOrthQ <- emmeans(FiSemQ, ~ Sem_Rel|SemRelQ)

```

```{r}
library(ggplot2)
library(ggrepel)
library(gridExtra)


OrthRelPlot <- emmip(FsOrthQ, Orth_Rel ~ OrthRelQ, CIs = TRUE, engine = "ggplot", plotit = FALSE)
OrthRelPlot$tvar <- as.factor(OrthRelPlot$tvar)
levels(OrthRelPlot$tvar) <- c(levels(OrthRelPlot$tvar), "Neighbor")
levels(OrthRelPlot$tvar) <- c(levels(OrthRelPlot$tvar), "Non-neighbor")
OrthRelPlot[OrthRelPlot == "related"] <- "Neighbor"
OrthRelPlot[OrthRelPlot == "unrelated"] <- "Non-neighbor"
print(OrthRelPlot)

SemRelPlot <- emmip(FsSemQ, Sem_Rel ~ SemRelQ, CIs = TRUE, engine = "ggplot", plotit = FALSE)
SemRelPlot$tvar <- as.factor(SemRelPlot$tvar)
levels(SemRelPlot$tvar) <- c(levels(SemRelPlot$tvar), "Related")
levels(SemRelPlot$tvar) <- c(levels(SemRelPlot$tvar), "Unrelated")
SemRelPlot[SemRelPlot == "related"] <- "Related"
SemRelPlot[SemRelPlot == "unrelated"] <- "Unrelated"
print(SemRelPlot)

Theme_Add <- theme_bw(base_size = 12, base_family = "") +
  theme(plot.title = element_text(color = "black",size=12)) +
  theme(panel.border = element_blank(), panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + 
  theme(axis.line = element_line(colour = "black", linetype = "solid")) +
  theme(legend.justification=c(1,0),legend.position=c(1,0.05), 
        legend.title=element_blank(),
        legend.text=element_text(colour="black", size=12),
        legend.key = element_rect((fill = NA))) +
  theme(axis.text.x = element_text(color = "black",size=12),
        axis.text.y = element_text(color = "black",size=12))

QuartileLabels <- c("1", "2", "3", "4" )


OrthRelQ_Plot <- ggplot(data = OrthRelPlot, aes(x = xvar, y=yvar, group = tvar))+
  geom_line(aes(linetype=tvar), size=1) +
  geom_point() +
  geom_errorbar(aes(ymin=LCL, ymax=UCL), width=.1) +
  geom_text_repel(aes(label=format(OrthRelPlot$yvar, digits=3)),
                  size = 4,direction = "y", hjust = 2, segment.colour = NA) +
  labs(title = "Orthographic Priming Effect", x = "Quantile", y = "RT (ms)") +
  scale_x_discrete(limits = c("X1", "X2", "X3", "X4"),labels= QuartileLabels) +
  Theme_Add
  

print(OrthRelQ_Plot)


SemRelQ_Plot <- ggplot(data = SemRelPlot, aes(x = xvar, y=yvar, group = tvar))+
  geom_line(aes(linetype=tvar), size=1) +
  geom_point() +
  geom_errorbar(aes(ymin=LCL, ymax=UCL), width=.1) +
  geom_text_repel(aes(label=format(SemRelPlot$yvar, digits=3)),
                  size = 4, direction = "y", hjust = 2, segment.colour = NA) +
  labs(title = "Semantic Priming Effect", x = "Quantile", y = "RT (ms)") +
  scale_x_discrete(limits = c("X1", "X2", "X3", "X4"),labels= QuartileLabels) +
  Theme_Add
  

print(SemRelQ_Plot)

ggsave("C:/Users/Alexander/Desktop/MyPlots/Exp1LongSOA_OrthRel.png", plot = OrthRelQ_Plot,scale = 1, width = 16, height = 9, units = c("cm"), dpi = 300, limitsize = TRUE)

ggsave("C:/Users/Alexander/Desktop/MyPlots/Exp1LongSOA_SemRel.png", plot = SemRelQ_Plot,scale = 1, width = 16, height = 9, units = c("cm"), dpi = 300, limitsize = TRUE)

#pdf("C:/Users/Alexander/Desktop/MyPlots/S64_LongSOA_OrthFacil.pdf", width = 12, height = 8)
#S64_LongSOA_OrthFacil <- grid.arrange(SemRelQ_Plot,OrthRelQ_Plot, ncol=2, nrow=1)
#dev.off() # Close the file


```


